利用方差的方差确定足够数量的Imputation:来自2012年NAMCS医师工作流程邮件调查的数据。

Qiyuan Pan, Rong Wei, Iris Shimizu, Eric Jamoom
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引用次数: 3

摘要

在多重推算中,多少次推算是足够的?不同研究人员给出的答案少则2 - 3,多则数百。也许没有单一数量的归咎能适用于所有情况。本研究采用2012年全国门诊医疗调查(NAMCS)医师工作流程邮件调查,根据插补数量m与插补方差标准误差ω之间的关系确定最小足够插补数η。测试了5个不同值范围、方差和缺失数据百分比的变量。对于所有测试的变量,ω随着m的增加而减小。在m值以上,进一步增加m的代价大于减小ω的好处,这一值被认为是η。任何人都有可能使用这种方法来确定适合自己数据情况的η值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Determining Sufficient Number of Imputations Using Variance of Imputation Variances: Data from 2012 NAMCS Physician Workflow Mail Survey.

How many imputations are sufficient in multiple imputations? The answer given by different researchers varies from as few as 2 - 3 to as many as hundreds. Perhaps no single number of imputations would fit all situations. In this study, η, the minimally sufficient number of imputations, was determined based on the relationship between m, the number of imputations, and ω, the standard error of imputation variances using the 2012 National Ambulatory Medical Care Survey (NAMCS) Physician Workflow mail survey. Five variables of various value ranges, variances, and missing data percentages were tested. For all variables tested, ω decreased as m increased. The m value above which the cost of further increase in m would outweigh the benefit of reducing ω was recognized as the η. This method has a potential to be used by anyone to determine η that fits his or her own data situation.

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